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4.2 Fine Tuning

In this lesson, students do not use LLMs. Instead, then engage in playful, hands-on/unplugged activities that focus on the crucial stage of fine-tuning LLMs, highlighting the human influence on their behavior and output.

4.1 Pre-training

In this lesson, students do not use LLMs. Instead, then engage in playful, hands-on/unplugged activities that aim to demystify how LLMs learn language by exploring concepts like tokenization, vectors, and attention mechanisms. It consists of three activities, each building upon the previous one.

Here is the image of a quesadilla with an algorithm creatively written on it. Enjoy the unique blend of culinary art and coding!

Best Quesadilla

I changed the food to be more culturally relevant to our school and our New Mexican culture. Instead of designing an algorithm for the best PB&J, this lesson tasks students with planning the best quesadilla. Our school population is majority Hispanic students who live in Albuquerque and mostly come from the South Valley. They will have more of a connection to quesadillas than to PB&J sandwiches. Optimizing a quesadilla algorithm for stakeholders may be more relevant to their culture and family.

Here is the image of an ethical quesadilla, beautifully presented with ethically sourced ingredients. Enjoy!

Quesadilla Ethical Matrix

For this lesson I changed the food listed to quesadillas (building on the previous lesson, Algorithms as Opinions), I also added a formative assessment to better help students connect to stakeholders in their lives and communities. Students who participate in the previous lesson (Algorithms as Opinions) will be able to carry it forward into a real-world connection by thinking about their out-of-school activity, the relevant stakeholders and their values.

Best Bocadillo

Making the best sandwich instead of PB & J for those who have not experienced PB & J. The reason I made this change is because there is a large Hispanic population in my area who do not make sandwiches with peanut butter and jelly and so a sandwich in general is more relevant to students of diverse populations such as Latinx, Asian, etc.

Neural Network Moves

These modifications were made to organize the procedure of the neural network game.

Decision Trees - Alien Gathering

Alien Gathering is a modification of the lesson plan 0.4 Decision Trees. In this lesson students begin with an activity in which a decision tree is made for a "Family" of aliens. This activity is intended to be used in one of two ways. It can be used as a substitute for the activity "Pastaland" or as an additional lesson activity used prior to the "Pastaland" activity.

What is AI? Analogy Teacher Made

This storyboard model provides a basic structure for integration of ELA and AI concepts. This activity is intended for students who have already been exposed to Poetry and the lesson called What is AI?

3.6 Roadmap to My Dream Job Lesson

In this activity, students will create their personal roadmaps to their dream jobs using what they have learned in the career sessions.

3.4 Environmental Impact of AI Lesson

In this lesson, students will explore the environmental impact of training AI models. Students will learn that the design of AI algorithms can have consequences for the environment.

3.3 Spread of Misinformation Lesson

In this lesson, students will be able to tell what misinformation is and understand that it spreads faster than authentic information. In the first lesson, students will play out a game in which they spread misinformation and reflect on their choices. In the second one, they will learn how to spot misinformation and come up with solutions on how to stop it.

3.1 Unanticipated Consequences of Technology Lesson

This lesson introduces students to the potential consequences of AI technologies and shows them that such consequences may or may not be the ones we intended or anticipated. Students will learn that AI technologies can have unanticipated effects on seemingly unrelated systems (e.g., social, cultural, environmental, etc.)

2.2 How do GANs Work? Lesson

This lesson introduces students to how GANs work as a result of the interplay between generator and discriminator neural networks. Students will learn how the generator and discriminator compete with one another to generate text, images, videos, and more.

2.1 What are GANs? Lesson

In this lesson, students will learn that GANs can generate art such as photographs, paintings, handwritten poetry, music, and jokes (that are kind of funny! Maybe.)

1.5 Planting the Seed of STEM Jobs Lesson

In this activity, students will learn about what STEM jobs are, why they should consider STEM jobs, and why it is important for everyone to participate in STEM jobs.

1.4 Inventory of Me Lesson

In this activity, students will learn about Holland’s work personality types and examples of jobs favored by people with each type. This lesson requires the students to use the Internet to answer a survey and explore a website.

0.6 Career Daydream Lesson

In this lesson, students will daydream about what a typical work day is going to be like in 30 years. The instructor will read a pre-written script to help students meditate and guide them to share their answers.

0.2 Algorithms as Opinions Lesson

This lesson introduces students to what an algorithm is, using the making of peanut butter jelly sandwiches as an example. Students will learn that an algorithm is like a recipe and that different people tend to prefer different algorithms based on their varied interests and goals.

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